Abstract
Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.
Highlights
In response to the requirement of opening market and to promote the economic growth, Taiwanese government has actively formulated policies to promote the financial liberalization in Taiwan
We propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks
The major contribution of this paper is to explore bank service satisfaction performance based on the 2013 service satisfaction survey data of 36 public and private banks in Taiwan
Summary
In response to the requirement of opening market and to promote the economic growth, Taiwanese government has actively formulated policies to promote the financial liberalization in Taiwan. Different from the existing studies on bank service satisfaction [1, 2], we use the data on service satisfaction of the public and private banks in Taiwan in this study. We used the grey relational analysis to examine the customer satisfaction on the service of the banks, including the public and the private banks in Taiwan. With the grey relational analysis, we found the variables that affected the levels of service satisfaction of the public banks and the private banks. We conducted a quantile regression analysis on the 36 banks of the dataset to find the variables that affected the levels of satisfaction for customers at different quantiles of service satisfaction. The results can be used by a bank manager to formulate different policies to further promote the satisfaction of the customers at different quantile of service satisfaction.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
